User Acceptance in Different Electronic Negotiation Systems - A Comparative Approach

Buettner, Ricardo; Daxenberger, Barbara; Woesle, Christian

doi:10.1109/icebe.2013.1

Cited by 23 publications

(6 citation statements)

References 100 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is a tremendous increase in professional company profiles on LinkedIn, XING and similar careeroriented online social networking markets (CSNM). Despite some specific challenges when contracting employees electronically (Buettner 2007a, b;Landes and Buettner 2011;Buettner et al 2013) the market figures, such as 433 mio. registered LinkedIn members, 4 mio.…”

Section: Introductionmentioning

confidence: 99%

Getting a job via career-oriented social networking markets

Buettner

2017

Electron Markets

Self Cite

View full text Add to dashboard Cite

While career-oriented social networking markets (CSNM) such as LinkedIn or XING are increasingly appreciated by a large number of internet users, our understanding of CSNM benefits and factors influencing the intention to use CSNM for a job search is very limited. For theoretical work on CSNM I extend Venkatesh's et al. UTAUT2 model by presenting a new concept considering a user's ties based on Granovetter's social network theory. The evaluation of the extended model, which asks users of CSNM about their job search behavior and their search success, shows a predictive quality increase from 19.0 percent to 80.5 percent. Post hoc analyses reveal a substantial negative relationship between the number of a user's ties and its job search success, which supports the experience of practitioners but contradicts scholarly findings. A level of about 150 contacts is most effective in terms of getting job offers, which confirms Dunbar's number. The results are useful for scholars and practitioners.

show abstract

Section: Introductionmentioning

confidence: 99%

Getting a job via career-oriented social networking markets

Buettner

2017

Electron Markets

Self Cite

View full text Add to dashboard Cite

show abstract

“…In terms of implementing the approach in real clinical environments we will conduct an implementation study to evaluate acceptance [80][81][82] and trust [83,84] by physicians and patients and if the automated approach improves the coordination [85,86] between physicians more efficiently.…”

Section: Future Workmentioning

confidence: 99%

Development of a Machine Learning Based Algorithm To Accurately Detect Schizophrenia based on One-minute EEG Recordings

Buettner¹,

Beil²,

Scholtz³

et al. 2020

Proceedings of the Annual Hawaii International Conference on System Sciences

Self Cite

View full text Add to dashboard Cite

While diagnosing schizophrenia by physicians based on patients' history and their overall mental health is inaccurate, we report on promising results using a novel, fast and reliable machine learning approach based on electroencephalography (EEG) recordings. We show that a fine granular division of EEG spectra in combination with the Random Forest classifier allows a distinction to be made between paranoid schizophrenic (ICD-10 F20.0) and nonschizophrenic persons with a very good balanced accuracy of 96.77 percent. We evaluate our approach on EEG data from an open neurological and psychiatric repository containing 499 one-minute recordings of n=28 participants (14 paranoid schizophrenic and 14 healthy controls). Since the fact that neither diagnostic tests nor biomarkers are available yet to diagnose paranoid schizophrenia, our approach paves the way to a quick and reliable diagnosis with a high accuracy. Furthermore, interesting insights about the most predictive subbands were gained by analyzing the electroencephalographic spectrum up to 100 Hz.

show abstract

“…To assess preconditions to implement the approach in real clinical environments we will conduct an implementation study to evaluate acceptance [72][73][74] and trust [75,76] by physicians and patients and determine if the automated approach improves the coordination [77][78][79] between doctors more efficiently.…”

Section: Future Workmentioning

confidence: 99%

High-performance Diagnosis of Sleep Disorders: A Novel, Accurate and Fast Machine Learning Approach Using Electroencephalographic Data

Buettner¹,

Grimmeisen²,

Gotschlich³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

Self Cite

View full text Add to dashboard Cite

While diagnosing sleep disorders by physicians using electroencephalographic data is protracted and inaccurate, we report promising results from a novel, fast and reliable machine learning approach. Our approach only needs an electroencephalographic recording snippet of 10 minutes instead of eight hours to correctly classify the disorder with an accuracy of over 90 percent. The Rapid Eye Movement sleep behavior disorder can lead to secondary diseases like Parkinson or Dementia. Therefore, it is important to classify the disorder fast and with a high level of accuracy -which is now possible with our approach.

show abstract

User Acceptance in Different Electronic Negotiation Systems - A Comparative Approach

Cited by 23 publications

References 100 publications

Getting a job via career-oriented social networking markets

Getting a job via career-oriented social networking markets

Development of a Machine Learning Based Algorithm To Accurately Detect Schizophrenia based on One-minute EEG Recordings

High-performance Diagnosis of Sleep Disorders: A Novel, Accurate and Fast Machine Learning Approach Using Electroencephalographic Data

Contact Info

Product

Resources

About